Recently, automatic speech recognition (ASR) research has seen a dramatic increase in the focus on sequence-to-sequence (seq2seq) models [1]}, [2]}.
These seq2seq models consist of a single neural network that is able to behave as both the acoustic and language model that would be used in a hybrid DNN-HMM system.
Additionally, these seq2seq models require no alignments, allowing for a much simpler training procedure.
While there has been a significant amount of research that shows these models can surpass hybrid systems with large amounts of training data [1]}, [4]}, [5]}, seq2seq models are less effective in cases where transcribed data is more limited [6]}.
